Combining dissimilarity measures for prototype-based classification

Prototype-based classification, identifying representatives of the data and suitable measures of dissimilarity, has been used successfully for tasks where interpretability of the classification is key. In many practical problems, one object is represented by a collection of different subsets of features, that might require different dissimilarity measures. In this paper we present a technique for combining different dissimilarity measures into a Learning Vector Quantization classification scheme for heterogeneous, mixed data. To illustrate the method we apply it to diagnosing viral crop disease in cassava plants from histograms (HSV) and shape features (SIFT) extracted from cassava leaf images. Our results demonstrate the feasibility of the method and increased performance compared to previous approaches.